No abstract

Auto rate adaptation mechanisms have been proposed to improve the throughput in wireless local area networks with IEEE 802.11a/b/g standards that can support multiple data rate at the physical layer. However, even with the capability of transmitting multi-packets with multi-rate IEEE 802.11 PHY, a mobile host near the fringe of the Access-Point's (AP's) transmission range still needs to adopt a low-level modulation to cope with the lower signal-to-noise ratio (SNR), Thus, it can not obtain a data rate as high as that of a host near AP in most cases. According to the characteristics of modulation schemes, the highest data rate between a pair of mobile hosts will be inversely proportional with the transmission distance. Considering these factors, we here demonstrate a RelayBased Adaptive Auto Rate (RAAR) protocol that can find a suitable relay node for data transmission between transmitter and receiver, and can dynamically adjust its modulation scheme to achieve the maximal throughput of a node according to the transmission distance and the channel condition. The basic concept is that the best modulation schemes are adaptively used by a wireless station to transmit an uplink data frame, according to the path loss condition between the station itself and a relay node, and that between the relay node and AP, thus delivering data at a higher overall data rate. Evaluation results show that this scheme provides significant throughput improvement for nodes located at the fringe of the AP's transmission range, thus remarkably improving overall system performance.

The aim of 5G wireless networks to provide Mbps and Gbps data rates to end users is expected to be fulfilled by the advanced technologies such as multi-input multi-output (MIMO), carrier aggregation (CA), inter/intra-cell communication, and adaptive modulation and coding techniques, which would be all realized in the Long Term Evolution-Advanced (LTE-A) heterogeneous network constituted by macrocells (MCs) and small cells (SCs) adopting these 5G advanced techniques. Given the potential of significantly increasing the network performance, the resource allocation (RA) problem involved becomes harder than ever especially when MIMO and CA are included in the RA problem involving multiple types of resources to be concurrently determined for the global optimization. Facing this challenge, we develop a framework to jointly optimize energy efficiency (EE), spectrum efficiency (SE), and queue length for downlink transmissions with an overall and comprehensive consideration of dynamically allocating resource blocks (RBs), component carriers (CCs), modulation and coding schemes (MCSs), and deciding user association (UA) with a power control (PC) mechanism on discrete power levels (PLs) in the heterogeneous LTE-based MIMO wireless networks. Specially, for the complex joint RA, UA, and PC problem, we conduct a mixed integer programming model to accommodate the stochastic optimization problem involved with the drift-plus-penalty (DPP) approach for Lyapunov opportunistic optimization. In particular, although it involves a nondeterministic polynomial time (NP) problem, we can still show a reduced problem to be solved easily through linear relaxation when its coefficient matrix is totally unimodular (TUM), and to be solved efficiently as well even when the TUM property is not guaranteed. Based on the reduction, we further develop a distributed or semi-distributed algorithm operated on two levels to approach the optimal results with lower complexity if the UA requirement can be relaxed. Finally, apart from exhibiting its performance on the weighting parameters, the numerical experiments also show our approach to make a good tradeoff among SE, EE, and queue length, and outperform the greedy-based state-of-the-art algorithms. INDEX TERMS LTE-A heterogeneous wireless networks, MIMO, carrier aggregation, multi-resource allocation, user association, power control.

No abstract